In many applications, the training data, from which one needs to learn a classifier, is corrupted with label noise. Many standard algorithms such as SVM perform poorly in presence of label noise. In this paper we investigate the robustness of risk minimization to label noise. We prove a sufficient condition on a loss function for the risk minimization under that loss to be tolerant to uniform l...

Twin support vector regression (TSVR), as an effective regression machine, solves a pair of smaller-sized quadratic programming problems (QPPs) rather than a single large one as in the classical support vector regression (SVR), which makes the learning speed of TSVR approximately 4 times faster than that of the SVR. However, the empirical risk minimization principle is implemented in TSVR, whic...

On recent trends, the reactive power planning problem has received a considerable amount of attention for allocation resources, both static and dynamic such as switchable capacitors and/or reactors, well Var compensators, respectively. The significant impact on voltage profile, stability transmission loss in systems. In years, instability is recognized major threat to system operation due short...

We describe, analyze, and experiment with a new framework for empirical loss minimization with regularization. Our algorithmic framework alternates between two phases. On each iteration we first perform an unconstrained gradient descent step. We then cast and solve an instantaneous optimization problem that trades off minimization of a regularization term while keeping close proximity to the re...

This paper presents the particle swarm optimization (PSO) algorithm for solving the optimal distribution system reconfiguration problem for power loss minimization. The proposed methodology determines control variable settings, such as the number of shunts to be switched, for real power loss minimization in the transmission system. The problem is formulated as a nonlinear optimization problem. ...